While automation is revolutionizing many aspects of biology, the determination of three-dimensional (3D) protein structure remains a long, hard, and expensive task. Novel algorithms and computational methods in biomolecular NMR are necessary to apply modern techniques such as structure-based drug design and structural proteomics on a much larger scale. Traditional (semi-) automated approaches to protein structure determination through NMR spectroscopy require a large number of experiments and substantial spectrometer time, making them dif - cult to fully automate. A chief bottleneck in the determination of 3D protein structures by NMR is the assignment of chemical shifts and nuclear Overhauser effect (NOE) restraints in a biopolymer. Therefore, we propose a novel attack on the assignment problem, to enable high-throughput NMR structure determination. Similarly, it is difficult to determine protein structures accurately using only sparse data. Sparse data arises not only in high-throughput settings, but also for larger proteins, membrane proteins, and symmetric protein complexes. New algorithms will be implemented to handle the increased spectral complexity and sparser information content obtained for such difficult proteins. The proposed research aims to minimize the number and types of NMR experiments that must be performed and the amount of human effort required to interpret the experimental results, while still producing an accurate analysis of the protein structure. The long-term goal of our project is to address key computational bottlenecks in NMR structural biology. In the past grant period, we have reported progress in automated assignments, novel algorithms for protein structure determination, characterization of protein complexes and membrane proteins, and fold recognition using only unassigned NMR data. We will develop novel geometric algorithms to improve and extend these techniques, focusing on four key areas: (a) Nuclear Vector Replacement (NVR), a molecular replacement-like technique for structure-based assignment; (b) sparse-data algorithms for protein structure determination from residual dipolar couplings (RDCs) using exact solutions and systematic search; (c) structure determination of membrane proteins and complexes, especially symmetric oligomers; and (d) automated assignment of NOE restraints in both monomers and complexes. We will develop and extend the software tools above in a set of integrated programs for automated fold recognition, assignment, monomeric and oligomeric structure determination. All programs will be tested on experimental NMR data, and new structures will be determined using our algorithms. [unreadable] [unreadable] Project Narrative [unreadable] [unreadable] While automation is revolutionizing many aspects of biology, the determination of three-dimensional protein structure remains a long, hard, and expensive task. Determination of protein structures by nuclear magnetic resonance (NMR) is valuable in many biomedical applications such as structure-based drug design. Since structural studies of proteins can not only provide clues to disease causes but also provide a basis for the rational design of therapeutic interventions, we propose novel algorithms and computational methods in biomolecular NMR, which are necessary to apply modern techniques such as structure-based drug design and structural proteomics on a much larger scale. [unreadable] [unreadable] [unreadable]